International educational migration as a “soft power resource” in the globalization era
Bibliographic record
Abstract
The international educational migration as a resource of «soft power» of the state has been analyzed in the article. Based on comprehensive analysis of the existing definitions of educational migration the author’s interpretation of this concept have been proposed. Based on the data of UNESCO, the Institute of international education of the United States, the Ministry of Science and Higher Education of the Russian Federation the statistics of international educational migration has been presented and analyzed. The main emphasis has been made on such categories of international educational migrants as students (bachelors, masters), postgraduate students. The reasons for the popularity of foreign students in countries such as Canada and the United States have been described. Based on the study two groups of factors have been highlighted: external and internal (motivational) factors, influencing decision-making in choosing the country of study. Based on the data of the Ministry of Science and Higher Education of the Russian Federation, the advantages of education in Russia have been analyzed. The issue of adaptation of foreign students in Russian universities has been considered: first-year curatorial programs, the Institute of student fellowships. It has been concluded hat Russian universities have a wealth of experience in teaching and adaptation of foreign students. The concepts and projects to attract foreign students to the Russian Federation also have been described in detail. Special attention to two projects “5–100” and “Export of Russian education” has been paid. The Federal Agency for the Commonwealth of Independent States Affairs, Compatriots Living Abroad, and International Humanitarian Cooperation (Rossotrudnichestvo) as one of the main institutions in the export of Russian education has been designated. The measures to attract foreign students to Russian universities have been proposed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".